Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System

We compare two approaches for a Markovian model in flexible manufacturing systems (FMSs) using Monte Carlo simulation. The model,which is a development of Fazlollahtabar and Saidi-Mehrabad (2013), considers two features of automated flexible manufacturing systemsequipped with automated guided vehicle (AGV), namely, the reliability of machines and the reliability of AGVs in a multiple AGVjobshop manufacturing system. The current methods for modeling reliability of a system involve determination of system state probabilitiesand transition states. Since the failure of the machines and AGVs could be considered in different states, a Markovian model is proposedfor reliability assessment. The traditional Markovian computation is compared with a neural network methodology. Monte Carlosimulation has verified the neural network method having better performance for Markovian computations

Keywords:

Reliability assessment , Markovian model , Neural network , Monte Carlo simulation